Application of intravascular optical coherence tomography in detection of coronary atherosclerotic plaque

doi:10.16352/j.issn.1001-6325.2022.03.002

Abstract

Abstract: Intravascular optical coherence tomography (IVOCT) technology has developed very rapidly in the detection of coronary atherosclerotic plaque in recent years. It can realize high-resolution, radiation-free and real-time in vivo imaging of intravascular diseased tissue, and plays an important role in clinical diagnosis. This paper briefly introduces the imaging principle of IVOCT, calculation methods of the optical characteristic parameters of biological tissue and the dispersion coefficient. The plaque recognition algorithm based on machine learning is also briefly summarized, so as to provide a new strategy and pathway for the development of constructing the artificial intelligent recognition system of plaque.

Key words: intravascular optical coherence tomography, atherosclerotic plaque, plaque recognition, optical characteristic parameters, machine learning

CLC Number:

R318.6

QIU Yao-yang, GUI Jia-hui, HUANG Lin, HU Xue-qiang, LI Qin. Application of intravascular optical coherence tomography in detection of coronary atherosclerotic plaque[J]. Basic & Clinical Medicine, 2022, 42(3): 360-365.

References

[1]杨卉,莫显刚,王兰,等.ApoE-/-小鼠动脉粥样硬化斑块中NHE1与Netrin-1表达上调[J].基础医学与临床,2020, 40: 777-783.
[2] Huang D, Swanson EA, Lin CP, et al. Optical coher-ence tomography[J]. Science, 1991, 254: 1178-1181.
[3]Tearney GJ, Brezinski ME, Bouma BE, et al. In vivo endoscopic optical biopsy with optical coherence tomography[J]. Science, 1997, 276: 2037-2039.
[4]Fujimoto JG, Schmitt JM, Swanson EA, et al. The development of OCT[M]//Ik-Kyung Jang. Cardiovascular OCT imaging. Entlebuch(Switzerland):Springer, 2015:1-22
[5]Qin N, Liu Y, Huang L, et al. Research on optical properties of cardiovascular tissues based on OCT data[J]. JInnov Opt Health Sci, 2021, 14:2140007-1-11.doi:10.1142/s17935458214000071.
[6]Schimitt JM, Knuttel A, Bonner RF. Measurement of optical properties of biological tissues by low-coherence reflectometry[J]. Appl Opt, 1993, 32: 6032-6042.
[7]van Soest G,Goderie T, Regar E, et al.Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging[J]. J Biomed Opt, 2010, 15: 011105.doi: 10.1117/1.3280271.
[8]Swaan A, Muller BG, Wilk LS, et al. One-to-one registration of en-face optical coherence tomography attenuation coefficients with histology of a prostatectomy specimen[J]. J Biophotonics, 2019, 12:e201800274. doi: 10.1002/jbio.201800274.
[9]Vermeer KA, Mo J, Weda J, et al. Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography[J]. Biomed Opt Express, 2013, 5: 322-337.
[10] Smith G, Dwork N, O'Connor D, et al. Automateddepth-resolved estimation of the attenuation coefficient from optical coherence tomography data[J]. IEEE Trans Med Imaging, 2015, 34: 2592-2602.
[11]Levitz D, Thrane L, Frosz M, et al. Determination of optical scattering properties of highly-scattering media in optical coherence tomography images[J]. Opt Express, 2004, 12: 249-259.
[12]Wang LV, Wu HI. Optical coherence tomography[M]//Wang LV, Wu HI. Biomedical optics: principles and imaging. Hoboken: John Wiley & Sons, Inc., 2007: 181-218.
[13]李鹏, 高万荣. 光学相干层析术提取色散信息的初步研究[J].光子学报, 2009, 38: 2598-2602.
[14]Wojtkowski M, Srinivasan VJ, Ko TH, et al. Ultrahigh-resolution, high-speed, Fourier domain optical coherence tomography and methods for dispersion compensation[J]. Opt Express, 2004, 12: 2404-2422.
[15]黄炳杰, 步鹏, 王向朝,等. 用于频域光学相干层析成像的深度分辨色散补偿方法[J]. 光学学报, 2012, 32:6-12.
[16]Liu D, Xin Y, Li Q, et al. Dispersion correction for optical coherence tomography by the stepped detection algorithm in the fractional Fourier domain[J]. Opt Express, 2020, 28: 5919-5935.
[17]Photiou C, Bousi E, Zouvani I, et al. Using speckle to measure tissue dispersion in optical coherence tomography[J]. Biomed Opt Express, 2017, 8: 2528-2535.
[18]Yabushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography[J]. Circulation, 2002, 106: 1640-1645.
[19]Kume T, Akasaka T, Kawamoto T, et al. Assessment of coronary arterial plaque by optical coherence tomography[J]. Am J Cardiol, 2006, 97: 1172-1175.
[20]Fujii K, Kawakami R, Hirota S. Histopathological validation of optical coherence tomography findings of the coronary arteries[J]. J Cardiol, 2018, 72: 179-185.
[21]Fujii K, Kubo T, Otake H, et al. Expert consensus statement for quantitative measurement and morphological assessment of optical coherence tomography[J]. Cardiovasc Interv Ther, 2020, 35: 13-18.
[22]Ughi GJ, Adriaenssens T, Sinnaeve P, et al. Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images[J]. Biomed Opt Express, 2013, 4: 1014-1030.
[23]Rico-Jimenez JJ, Campos-Delgado DU, Villiger M, et al. Automatic classification of atherosclerotic plaques imaged with intravascular OCT[J]. Biomed Opt Express, 2016, 7: 4069-4085.
[24]Gessert N, Lutz M, Heyder M, et al. Automatic plaque detection in IVOCT pullbacks using convolutional neural networks[J]. IEEE Trans Med Imaging, 2018, 38: 426-434.
[25]Gharaibeh Y, Prabhu DS, Kolluru C, et al. Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification scoring[J]. J Med Imaging, 2019, 6: 045002.doi: 10.1117/1.JMI.6.4.045002
[26]Liu X, Du J, Yang J, et al. Coronary artery fibrous plaque detection based on multi-scale convolutional neural networks[J]. J Signal Process Syst, 2020, 92: 325-333.
[27]Guo X, Tang D, Molony D, et al. A machine learning-based method for intracoronary OCT segmentation and vulnerable coronary plaque cap thickness quantification[J]. Int J Comput Methods, 2019, 16: 1842008.doi: 10.1142/S0219876218420082.
[28]Liu R, Zhang Y, Zheng Y, et al. Automated detection of vulnerable plaque for intravascular optical coherence tomography images[J]. Cardiovasc Eng Technol, 2019, 10: 590-603.
[29]Wang J. OCT image recognition of cardiovascular vulnerable plaque based on CNN[J]. IEEE Access, 2020, 8: 140767-140776.
[30]Shi P, Xin J, Liu S, et al. Vulnerable plaque recognition based on attention model with deep convolutional neural network[C]. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018: 834-837. doi: 10.1109/EMBC.2018.8512279.